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Vehicle manufacturers face increasing challenges in auditing the build quality of their vehicles while considering increasing consumer demands regarding NVH performance. This effect is compounded with the rise in electric and hybrid vehicles. The ability to audit vehicles for a variety of noise types is becoming increasingly important; these include powertrain noise, road noise, and wind noise. An automated measurement system was developed with specific algorithms and sound quality metrics to not only audit vehicle production quality but to add objective data, pass-fail criteria, and trend analysis.

Source-path-contribution (SPC) analysis, or transfer-path-analysis, is a test based method to characterize noise and vibration contributions of a complex system. The methodology allows for the user to gain insight into the structural forces and acoustic source strengths that are exciting a system, along with the effects of the structural and acoustic paths between each source and a receiver position. This information can be utilized to understand which sources and/or paths are dominating the noise and vibration performance of a system, allowing for focused target cascading and streamlined troubleshooting efforts. The SPC process is widely used for automotive applications, but is also applicable for a wide range of product types. For each unique application the basic SPC principles remain constant, however best practices can vary for both measurement and analysis depending on the type of system being evaluated.

Binaural recordings are often used for added realism in subjective listening studies, but are commonly played back in environments that are different than those in which the recordings were taken. An important component of the added realism is the ability of the listener to locate the acoustic sources in a three dimensional space. While humans can generally do a good job of locating acoustic sources through inter-aural time differences (ITD) and inter-aural intensity differences (IID), some well documented ambiguities exist when using these acoustic cues by themselves (i.e. ITD and ILD for a source in front of or behind a listener are identical). To resolve these ambiguities, humans often rely on supplemental information from either direct visual feedback or from their knowledge of and comfort with the listening environment.

The role of NVH test development has changed from addressing a system-level NV concern late in the design cycle (firefighting) to having well established NV optimized test procedures in place. One way this is achieved is by leveraging the information gained during troubleshooting of current product to improve the future product development process for noise and vibration. Today, most NV groups/laboratories use optimized test procedures for creating accurate, consistent, and efficient test results. This still requires expertise to post-process data, compute targets and interpret results to guide product development. This step is often overlooked and, in recent years, due to the lack of NV expertise of “younger” labs (typically in non-automotive industries) or of more established labs affected by the economic downturn (early retirements, lay-offs, especially in the automotive industry) there has been a growing need for automated post-processing “intelligent” procedures.

Design parameters for automotive components can be highly affected by the requirements imposed for vehicle pass-by compliance. The key systems affecting pass-by performance generally include the engine, tires, intake system, and exhaust system. The development of these systems is often reliant on the availability of prototype hardware for physical testing on a pass-by course, which can lead to long and potentially costly development cycles. These development cycles can benefit significantly from the ability to utilize analytical data to guide development of component-level design parameters related to pass-by noise. To achieve this goal, test and analysis methods were developed to estimate the vehicle-level pass-by performance from component level data, both from physical and/or analytical sources. The result allows for the estimation of the overall vehicle-level pass-by noise along with the contributions to the total and dominant frequency content from each of the key noise sources.

As an agricultural tractor OEM was moving a new tractor model from development into production, an objectionable cab “boom” was identified that was not present in the preproduction pilot -level tractors. The cab boom was identified as a low frequency tone causing an increase of 7 (dBA) over the level in the pilot tractors, which was deemed unacceptable. The process used by the tractor OEM engineering team to address this problem has been widely used and refined in the automotive industry, but it is relatively new in the agricultural/off-road vehicle industry. This paper describes the source-path-receiver approach that led to identifying the exhaust tip as the source and the vibro-acoustic coupling of a windshield structural mode with an acoustic cab cavity mode as the path of the boom event.

For the development of a self-propelled crop spraying machine, a hybrid experimental and analytical Source-Path-Contribution (SPC) approach is utilized by a leading agricultural equipment manufacturer. The objective is to predict noise and sound quality in the cab before prototypes are assembled, so that dB(A) and SQ targets can be assessed early on and better specifications sent to suppliers to achieve these vehicle-level targets. The experimental SPC task is conducted on the current crop sprayer model, which has the same cab but different engine, transmission and hydraulics than the new model. A hybrid FE-SEA model of the current cab is developed and run at load cases derived from test data. The SEA approach is needed to evaluate the effect of cab acoustic treatments, which are not accounted for in the SPC experimental model. Contributions to in-cab noise for the current sprayer are estimated from both experimental and analytical SPC.

Shudder vibration of a hydraulic power steering system during parking maneuver was studied with numerical and experimental methods. To quantify vibration performance of the system and recognize important stimuli for drivers, a shudder metric was derived by correlation between objective measurements and subjective ratings. A CAE model for steering wheel vibration analysis was developed and compared with measured data. In order to describe steering input dependency of shudder, a new dynamic friction modeling method, in which the magnitude of effective damping is determined by average velocity, was proposed. The developed model was validated using the measured steering wheel acceleration and the pressure change at inlet of the steering gear box. It was shown that the developed model successfully describes major modes by comparing the calculated FRF of the hydraulic system with measured one from the hydraulic excitation test.

In the world of BSR (Buzz, Squeak and Rattle) testing, there is a high level of sophistication regarding the test machines employed to excite the items under test as well as the techniques used to ensure that the test is representative of real-life operating conditions. However, the object of the measurements, i.e., the identification of transient acoustic events classified as Buzz, Squeak or Rattle, is mostly a subjective procedure with classification in terms of Sound Pressure Level in dB(A) or Stationary Loudness. These “standard” metrics have proven, in general, unreliable in assessing the importance of individual transient events, and inappropriate to describe the vehicle signature from a BSR standpoint.

Product development processes generate large quantities of experimental and analytical data. The data evaluation process is usually quite lengthy since the data needs to be extracted from a large number of individual output files and arranged in suitable formats before they can be compared. When the data quantity grows extremely large, manual extraction cannot be done in a limited timeframe. This paper describes a set of tools developed by MTS engineers to automatically extract the desired information from a large number of files and perform data post-processing. The tools greatly improved both speed and accuracy of the evaluation process during the development of a sound quality-based end-of-line inspection system for seat tracks [1]. It allowed engineers to quickly gather a comprehensive understanding of the relative importance of individual design parameters and of their correlation to the subjective perception of the sound quality of the seat track.

In recent years, the perceived quality of powered seat adjusters based on their sound during operation has become a primary concern for vehicle and seat manufacturers. Historical noise targets based on overall dB(A) at the occupant's ear have consistently proved inadequate as a measure of the sound quality of a seat adjuster. Significant effort has been devoted to develop alternative sound quality metrics that can truly discriminate between “good” and “bad” seat adjusters. These new metrics have been successfully applied for some years by product development engineers in test labs. However, in the assembly plant the sound quality of the seat adjuster is still assessed subjectively by an operator at the end of the assembly line. The main problem with this approach is not only the lack of consistency and repeatability across large samples of seat tracks, but also the fact that the only feedback provided from the end-of-line to the product development team is of subjective nature.